Artificial intelligence in business

GrAIt expectations

Artificial intelligence is spreading beyond the technology sector, with big consequences for companies, workers and consumers, says Alexandra Suich Bass

商业中的人工智能

远大前程

人工智能的传播已超越技术领域，给企业、员工及消费者带来重大影响，本文作者亚历山德拉·苏伊希·巴斯观察认为

LIE DETECTORS ARE not widely used in business, but Ping An, a Chinese insurance company, thinks it can spot dishonesty. The company lets customers apply for loans through its app. Prospective borrowers answer questions about their income and plans for repayment by video, which monitors around 50 tiny facial expressions to determine whether they are telling the truth. The program, enabled by artificial intelligence (AI), helps pinpoint customers who require further scrutiny.

测谎仪并未在企业中广泛应用，但中国平安保险公司相信自己能探测谎言。这家公司让客户通过它的一款应用程序来申请贷款。未来的贷款人在视频中回答有关收入和还款计划的问题。视频会监测他们的大概50个细微面部表情，判断他们是否在说真话。这套人工智能（AI）驱动的程序帮助筛查出需要进一步审核的客户。

AI will change more than borrowers’ bank balances. Johnson & Johnson, a consumer-goods firm, and Accenture, a consultancy, use AI to sort through job applications and pick the best candidates. AI helps Caesars, a casino and hotel group, guess customers’ likely spending and offer personalised promotions to draw them in.

Bloomberg, a media and financial-information firm, uses AI to scan companies’ earnings releases and automatically generate news articles. Vodafone, a mobile operator, can predict problems with its network and with users’ devices before they arise. Companies in every industry use AI to monitor cyber-security threats and other risks, such as disgruntled employees.

AI将改变的不仅仅是贷款人的账户余额。消费品公司强生和咨询公司埃森哲（Accenture）用AI查看应聘资料，筛选出最佳人选。AI帮助赌场和酒店集团凯撒娱乐（Caesars）估测客人的消费水平，提供个性化促销来吸引他们。媒体和金融信息公司彭博用AI扫描企业财报，自动生成新闻报道。移动运营商沃达丰（Vodafone）用AI监测其网络和用户设备，提前预警故障。各行各业的公司都在使用AI监控网络安全威胁和其他风险，比如心怀不满的员工。

Instead of relying on gut instinct and rough estimates, cleverer and speedier AI-powered predictions promise to make businesses much more efficient. At Leroy Merlin, a French home-improvement retailer, managers used to order new stock on Fridays, but defaulted to the same items as the week before so they could start their weekend sooner.

The firm now uses algorithms to take in past sales data and other information that could affect sales, such as weather forecasts, in order to stock shelves more effectively. That has helped it reduce its inventory by 8% even as sales have risen by 2%, says Manuel Davy of Vekia, the AI startup that engineered the program.

相比依赖直觉和粗略的估算，更聪明也更快速的AI预测将帮助企业大幅提高效率。法国家居装饰零售商乐华梅兰（Leroy Merlin）的管理层以前每周五下新订单，默认的设置是重复前一周的订单，这样大家可以早点下班过周末。现在，公司用算法来斟酌历史销售数据和天气预报等其他可能影响销售的信息，以更有效地安排库存。据创建该算法的AI创业公司Vekia的曼纽尔·戴维（Manuel Davy）说，这帮助该公司将库存减少了8％，同时销售额却增长了2％。

AI and machine learning (terms that are often used interchangeably) involve computers crunching vast quantities of data to find patterns and make predictions without being explicitly programmed to do so. Larger quantities of data, more sophisticated algorithms and sheer computing power have given AI greater force and capability. The outcomes are often similar to what an army of statisticians with unlimited time and resources might have come up with, but they are achieved far more quickly, cheaply and efficiently.

AI和机器学习（这两个术语常被混用）用计算机处理查看海量数据，从中找出模式并做出预测，而不需要编程来作出明确的指示。更多数据、更复杂的算法和更高的计算能力已经赋予了AI更强大的能力。它得出的结果往往和一大批拥有无限时间和资源的统计师所得的差不多，但它远为快速、便宜和高效。

One of AI’s main effects will be a dramatic drop in the cost of making predictions, says Ajay Agrawal of the University of Toronto and co-author of a new book, “Prediction Machines”. Just as electricity made lighting much more affordable—a given level of lighting now costs around 400 times less than it did in 1800—so AI will make forecasting more affordable, reliable and widely available.

AI的主要成果之一将是令做预测的成本大幅下降，新书《预测机器》（Prediction Machines）的合著者、多伦多大学的阿杰伊·阿格拉沃尔（Ajay Agrawal）表示。就像电力让照明变得便宜了许多——如今的照明成本比1800年时低400倍左右——AI会让做预测更便宜、更可靠、更普及。

Computers have been able to read text and numbers for decades, but have only recently learned to see, hear and speak. AI is an omnibus term for a “salad bowl” of different segments and disciplines, says Fei-Fei Li, director of Stanford’s AI Lab and an executive at Google’s cloud-computing unit. Subsections of AI include robotics, which is changing factories and assembly lines, and computer vision, used in applications from identifying something or someone in a photo to self-driving-car technology.

Computer vision is AI’s “killer app”, says Ms Li, because it can be used in so many settings, but AI has also become more adept at recognising speech. It underlies voice assistants on phones and home speakers and allows algorithms to listen to calls and take in the speaker’s tone and content.

计算机能阅读文本和数字已经有几十年了，但直到最近才学会了看、听、说。AI是一个综合性术语，就像是涵盖了不同领域和学科的“一碗色拉”，斯坦福大学人工智能实验室主管、谷歌云计算部门负责人李飞飞说。它的下属分支包括正在改变工厂和组装线的机器人技术，以及部署在各种应用程序中的计算机视觉——从识别照片中的人或物到无人驾驶汽车技术等。李飞飞说，计算机视觉是AI的“杀手级应用”，因为运用场合是如此之多，但AI在语音识别方面也已变得更加娴熟。它是配备在手机和家用音箱上的语音助理的技术基础，还让算法能够监听来电并识别说话者的语调和内容。

Techtonic shifts

技术板块漂移

Until now the main beneficiary of AI has been the technology sector. Most of today’s leading tech firms, such as Google and Amazon in the West and Alibaba and Baidu in China, would not be as big and successful without AI for product recommendations, targeted advertising and forecasting demand.

Amazon, for example, uses AI widely, for tasks such as guiding robots in its warehouses and optimising packing and delivery, as well as detecting counterfeit goods and powering its speaker, Alexa. Alibaba, a Chinese rival, also makes extensive use of AI, for example in logistics; and its online-payments affiliate, Ant Financial, is experimenting with facial recognition for approving transactions. Sundar Pichai, Google’s boss, has said that AI will have a “more profound” impact than electricity or fire.

到目前为止，AI的主要受益者一直是技术部门。如果没有AI帮助实现产品推荐、定向广告和需求预测，那么当今大多数科技领军企业，比如西方的谷歌和亚马逊以及中国的阿里巴巴和百度，都不会发展到今天这般庞大且成功。

举例来说，亚马逊广泛使用AI来完成各种任务，诸如在仓库中指示机器人工作、优化包装和运送、检测假货，支持其智能音箱Alexa等。它的竞争对手、中国的阿里巴巴也在物流等部门广泛运用AI，其在线支付分支机构蚂蚁金服正测试使用面部识别来核准交易。谷歌执行长桑达尔·皮查伊（Sundar Pichai）曾经说过，AI将产生比电和火“更深远”的影响。

Bosses of non-tech companies in a broad range of industries are starting to worry that AI could scorch or even incinerate them, and have been buying up promising young tech firms to ensure they do not fall behind. In 2017 firms worldwide spent around $21.8bn on mergers and acquisitions related to AI, according to PitchBook, a data provider, about 26 times more than in 2015 (see chart). They are doing this partly to secure talent, which is thin on the ground. Startups without revenue are fetching prices that amount to $5m-10m per AI expert.

然而各行各业的非科技公司老板们已经开始担心，AI可能会冲击甚至毁灭自己，因而他们纷纷收购看来颇有前途的年轻科技公司，以确保自己不落人后。据数据供应商PitchBook统计，2017年全球企业在AI相关并购上的支出约达218亿美元，比2015年增长约26倍（见图表）。它们这样做的部分原因是为获得目前仍相当稀缺的AI人才。尚未产生收入的创业公司为聘请一名AI专家花费多达500万至1000万美元。

As AI spreads beyond the tech sector, it will fuel the rise of new firms that challenge incumbents. This is already happening in the car industry, with autonomous-vehicle startups and ride-hailing firms such as Uber. But it will also change the way other companies work, transforming traditional functions such as supply-chain management, customer service and recruitment.

随着AI传播到科技行业之外，它将推动新企业的崛起，为成熟企业带来挑战。这已经在汽车产业里发生——AI催生了无人驾驶汽车创业公司和优步等网约车公司。但它也将改变其他企业的运作方式，改变供应链管理、客服和招聘等传统职能。

The path ahead is exhilarating but perilous. Around 85% of companies think AI will offer a competitive advantage, but only one in 20 is “extensively” employing it today, according to a report by MIT’s Sloan Management Review and the Boston Consulting Group. Large companies and industries, such as finance, that generate a lot of data, tend to be ahead and often build their own AI-enhanced systems. But many firms will choose to work with the growing array of independent AI vendors, including cloud providers, consultants and startups.

前路令人振奋却也危险重重。根据麻省理工学院的《斯隆管理评论》和波士顿咨询集团联合撰写的报告，约85％的企业认为AI将带来竞争优势，但只有5％的公司正在“广泛”地使用它。生成大量数据的大企业和金融等行业往往走在前头，常常建立自己的AI增强系统。但许多企业会选择与队伍不断扩大的独立AI供应商合作，包括云供应商、咨询公司和创业公司等。

This is not just a corporate race but an international one, too, especially between America and China. Chinese firms have an early edge, not least because the government keeps a vast database of faces that can help train facial-recognition algorithms; and privacy is less of a concern than in the West.

这不仅是一项企业竞赛，也是一场国际竞逐——尤其在中美之间。中国企业有一个先发优势，这主要是因为中国政府拥有一个庞大的人脸数据库，可以用来训练面部识别算法。而且，与西方相比，中国人对隐私也不那么关切。

There will be plenty of opportunities to stumble. One difficult issue for companies will be timing. Roy Bahat of Bloomberg Beta, a venture-capital firm, draws a parallel between now and the first dotcom boom of the late 1990s: “Companies are flailing to figure out what to spend money on.” If they invest huge sums in AI early on, they run the risk of overcommitting themselves or paying large amounts for worthless startups, as many did in the early days of the internet. But if they wait too long, they may leave themselves open to disruption from upstarts, as well as from rivals that were quicker to harness technology.

跌跤的机会很多。企业面临的难题之一是对时机的把握。风险投资公司Bloomberg Beta的罗伊·巴哈特（Roy Bahat）把眼下的状况比作上世纪90年代末的首个互联网泡沫期：“对于该往哪儿投钱，企业无所适从。”如果它们早早地在AI上投入巨资，就要冒对一文不值的创业公司过度依赖或为之浪费大笔金钱的风险，就像互联网早期许多公司的经历那样。但如果它们等得太久，又有可能把自己置于被市场新贵颠覆的境地，还可能被更快掌握了新技术的竞争对手冲击。

Some may have been misled by glowing media reports, believing AI to be a magic wand that can be installed as easily as a piece of Microsoft software, says Gautam Schroff of Tata Consultancy Services, an Indian firm. AI systems require thorough preparation of data, intensive monitoring of algorithms and a lot of customisation to be useful. Gurdeep Singh of Microsoft speaks of AI systems as “idiots savants”;

they can easily do jobs that humans find mind-boggling, such as detecting tiny flaws in manufactured goods or quickly categorising millions of photos of faces, but have trouble with things that people find easy, such as basic reasoning. Back in 1956, when academic researchers held their first gathering to discuss AI, they were looking for a way to imbue machines with human-like “general” intelligence, including complex reasoning. But that remains a distant aspiration.

还有些企业可能被媒体天花乱坠的报道误导，以为AI就是一根魔法棒，像微软的软件一样容易安装，印度公司塔塔咨询服务（Tata Consultancy Services）的高塔姆·施罗夫（Gautam Schroff）说。AI系统需要全面细致地准备数据、深入地监测算法和大量的定制才能发挥用处。

微软的格迪普·辛格（Gurdeep Singh）称AI系统为“白痴专家”——它们能轻易完成让人类望而却步的艰巨任务，比如检测制成品中的细小瑕疵，或给数百万张人脸照片快速分类，但在那些对人类而言轻而易举的任务上（比如基本推理）却遇到麻烦。早在1956年学术研究人员举行首次AI研讨会时，他们就在寻找办法来赋予机器像人类那样的“一般”智能，包括复杂推理的能力。但直到今天，这仍是一种遥遥无期的向往。

The excitement around AI has made it hard to separate hype from reality. In the last quarter of 2017 public companies across the world mentioned AI and machine learning in their earnings reports more than 700 times, seven times as often as in the same period in 2015 (see chart). There are so many firms peddling AI capabilities of unproven value that someone should start “an AI fake news” channel, quips Tom Siebel, a Silicon Valley veteran.

AI引发的兴奋之情使得我们难以分辨炒作和现实。2017年最后一个季度，全球上市公司在它们的财报中提到AI和机器学习多达700多次，是2015年同期的七倍（见图表）。硅谷资深人士汤姆·西贝尔（Tom Siebel）开玩笑说，这么多公司在兜售尚未证实价值的AI技术，应该有人来开办一个“AI假新闻”频道了。

Bosses must keep several time horizons in mind. In the near future AI will reshape traditional business functions such as finance, HR and customer service, according to Michael Chui of the McKinsey Global Institute, a think-tank within a consultancy. But over time it will also disrupt whole industries, for example by powering the rise of autonomous vehicles or the discovery of entirely new drug combinations. Whereas humans may have preconceptions about which product designs or drug combinations are likely to work best, algorithms are more likely to come up with novel solutions.

老板们须谨记几个时间段。根据咨询公司麦肯锡下属智库麦肯锡全球研究院的迈克尔·崔（Michael Chui）的说法，在不久的将来，AI将重塑企业的传统职能，比如财务、人事和客服。但随着时间的推移，它也将颠覆行业整体，比如通过推动无人驾驶汽车兴起和发现全新的药物组合等。人类对于哪种产品设计或药物组合可能取得最佳效果也许已有成见，而算法更可能提出全新的解决方案。

In private, many bosses are more interested in the potential cost and labour savings than in the broader opportunities AI might bring, says John Hagel of Deloitte, a consultancy. That is certainly not good for workers, but nor, ultimately, is it good for business. “If you just cut costs and don’t increase value for customers, you’re going to be out of the game,” he says.

Some companies may not actually eliminate existing jobs but use technology to avoid creating new ones. And workers who keep their jobs are more likely to feel spied on by their employers. Some firms already use AI to comb through their workers’ communications to ensure that they are not breaking the law. Such practices will spread, raising privacy issues.

咨询公司德勤的约翰·哈格尔（John Hagel）说，私下里，很多企业老板更为关注AI能帮助节省多少成本和劳动力，而不是它带来的更广泛的机会。这对员工来说肯定不是好事，但最终也会对企业不利。“如果你只是削减成本而不增加为客户带来的价值，那你就会被淘汰。”他说。一些企业或许并不会削减现有岗位，但会利用技术来避免增员。而那些保住了工作的工人更可能感到被雇主监视。一些公司已经在使用AI查看员工的聊天记录，以确保他们不违法。这类做法将日益普遍，从而引发隐私问题。

A longer-term concern is the way AI creates a virtuous circle or “flywheel” effect, allowing companies that embrace it to operate more efficiently, generate more data, improve their services, attract more customers and offer lower prices. That sounds like a good thing, but it could also lead to more corporate concentration and monopoly power—as has already happened in the technology sector.

从更长远的视角看，我们要担忧AI将创造良性循环或“惯性轮”效应：它会使那些采纳它的企业更高效地运营，生成更多数据，改善服务，吸引到更多客户，提供更低的价格。这听起来像是一件好事，但它会导致更多企业整合和垄断——就像科技领域已经发生的那样。

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